Credit data analysis

ABSTRACT

A computer system receives credit information relating to a consumer and a number of high scorers, and determines credit score factors associated with the consumer and the high scorers. The system may construct flippable score factor displays comprising consumer specific information specific to a credit category that may be reversed to display explanatory text regarding how that credit category affects their credit report. The score factor display may include a comparison between the consumer&#39;s scores and the high scorers&#39; scores in a number of categories. Scores of high scorers may be periodically refreshed. Additionally, the group of high scorers may be limited according to a particular demographic, such as a geographic location, that may be selectable by a user.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.13/826,118, filed on Mar. 14, 2013, which claims priority fromprovisional U.S. Pat. App. No. 61/732,244, filed on Nov. 30, 2012, whichis hereby incorporated by reference in its entirety.

This application is related to, but does not claim priority from, U.S.patent application Ser. No. 10/452,155, filed May 30, 2003, now U.S.Pat. No. 7,610,229; U.S. patent application Ser. No. 12/606,060, filedOct. 26, 2009, now U.S. Pat. No. 8,015,107; U.S. patent application Ser.No. 11/150,480, filed Jun. 10, 2005, now U.S. Pat. No. 7,593,891; U.S.patent application Ser. No. 12/563,779, filed Sep. 21, 2009, now U.S.Pat. No. 7,925,582; U.S. patent application Ser. No. 13/326,803, filedDec. 15, 2011, and U.S. Prov. Pat. App. No. 60/384,650, filed May 30,2002. The disclosures of the above-listed applications are all herebyincorporated by reference, for all purposes, as if set forth herein intheir entireties.

BACKGROUND

This disclosure relates to the field of consumer credit information andparticularly to the presentation of credit score and credit reportinformation.

A credit score is an important indicator of a consumer's financialhealth. Consequently, having a high credit score is important toconsumers for many reasons. A high credit score may qualify a consumerfor various financial programs and/or allow a consumer to receivefavorable rates is such programs, such as loan applications, rentalapplications, real estate mortgages, and so on. A credit report mayallow a user to view the underlying data affecting their credit score.Thus, many consumers have a substantial interest in finding ways toimprove their credit scores.

There is much information available to consumers as to how to improvecredit scores. For example, sources provide advice to consumers to payoff loans, to establish certain numbers of credit accounts, to establishnew loans, to raise or lower credit card limits, and so on. However,this advice is generic to all consumers and does not provide informationspecific to a particular consumer's situation. The question for manyconsumers then is “How is my particular credit data affecting my creditscore?”

To determine effective actions to take, it is often necessary to analyzea consumer's underlying credit information. However, paper creditreports are often confusing to consumers, and do not explain the effectsof particular pieces of data contained within the credit report.Furthermore, consumers often do not know how their credit activitiesaffect their credit score or how their credit score is calculated. Thistranslates into consumers struggling to use and analyze their creditinformation in meaningful ways, or develop effective strategies to raisetheir credit score.

SUMMARY

Thus, it would be advantageous for consumers to be able to quickly viewand understand how credit information and/or credit data affects theircredit score. In particular, it would be advantageous for consumers tobe able to visualize their particular credit data and understand how itis affecting their current credit score. It would also be advantageousfor consumers to visualize the impact of specific credit data upon theircredit score, either individually, or by category.

Consumers are interacting more every day with mobile devices, such assmart phones, tablets, and the like. However, information that consumerstraditionally view in the form of printed materials don't optimizeinteraction capabilities of such mobile devices. Discussed herein aresystems and methods for generating user interfaces that display creditinformation of consumers in manners that are specifically tailored foroptimal use on mobile devices, such as user interfaces that optimize theuser's ability to interface with credit data and to explore such creditdata.

In an embodiment, a mobile application may display and receive inputfrom various user interfaces, including a flippable user interface thatdisplays consumer specific credit information values and high scorersvalues in a variety of credit categories. High scorers values indicaterepresentative credit information for archetypal individuals who areconsidered low risk by lenders and/or have excellent credit scores. Theflippable user interface allows a user to touch a flippable userinterface and cause it to display, on a reverse side, informationcorresponding to the credit category that explains how that creditcategory affects their credit score. The mobile application thus enablesa user to compare their own credit-related scores in various creditcategories to the scores of high scorers to determine areas in whichtheir scores are low and may be improved and/or areas in which theirscores are high.

In an embodiment, a computer-implemented method of electronic creditdata analysis in an electronic environment is disclosed. The methodcomprises: as implemented by one or more computer systems comprisingcomputer hardware and memory, the one or more computer systemsconfigured with specific executable instructions, receiving a requestfrom a requestor for an electronic consumer credit analysis; accessing,from an electronic data store over a network, consumer credit dataassociated with a plurality of consumers, wherein the requestor is oneof the plurality of consumers; designating a plurality of high scorersfrom the plurality of consumers, the one or more high scorers eachhaving associated credit scores that exceed a predetermined threshold;calculating, with a processor, for each of one or more creditcategories, a high scorer score based at least in part on consumercredit data associated with the plurality of high scorers; andtransmitting, over the network, to the requestor, the electronicconsumer credit analysis including the one or more credit categories,the one or more high scorer scores for the respective credit categories,and consumer credit data associated with the requestor for therespective credit categories.

According to an aspect, calculating a high scorer score for a particularcategory comprises averaging the consumer credit data of the pluralityof high scorers within the particular category.

According to another aspect, the one or more credit categories includesat least one of a number of maxed-out credit cards, a number of publicrecords, a number of credit inquiries, an average credit card limit, anaverage age of accounts, a mortgage standing, a number of missedpayments, a number of open credit cards, number of installment loans, acredit-to-debt ratio, an oldest account age, and a credit file updatetime.

According to yet another aspect, the computer-implemented method furthercomprises: as further implemented by the one or more computer systems,determining a relevant demographic, wherein said designating one or morehigh scorers includes designating only high scorers associated with therelevant demographic.

According to another aspect, the relevant demographic includes at leastone of an age associated with the requestor, a gender associated withthe requestor, an ethnicity associated with the requestor, an employmentstatus associated with the requestor, a geographic location associatedwith the requestor, a net worth associated with the requestor, and anincome level associated with the requestor.

According to yet another aspect, the relevant demographic comprises ageographic location associated with the requestor, and wherein thegeographic location associated with the requestor includes at least oneof a neighborhood in which the requestor lives, a city in which therequestor lives, a county in which the requestor lives, a state in whichthe requestor lives, and a country in which the requestor lives.

According to another aspect, the computer-implemented method furthercomprises: as further implemented by the one or more computer systems,causing the electronic consumer credit analysis to be displayed in auser interface on a mobile computing device associated with therequestor.

According to yet another aspect, the computer-implemented method furthercomprises: determining the one or more credit categories based at leastin part on the accessed consumer credit data.

According to another aspect, the high scorer score is periodicallyrecalculated and retransmitted to the requestor.

In another embodiment, a computer system is disclosed which comprises:one or more hardware processors in communication with a computerreadable medium storing software modules including instructions that areexecutable by the one or more hardware processors, the software modulesincluding at least: a user interface module configured to receive, froma consumer, a request for a credit score analysis; a data collectionmodule configured to retrieve, from an electronic credit data store,credit information associated with the consumer and a plurality of otherconsumers; and an analysis module configured to determine a set of highscorers from the plurality of other consumers, and determine, for eachof a plurality of score factors, a consumer score and a high scorersscore, wherein the user interface module is further configured toprovide, to the consumer, an analysis comprising, for each of theplurality of score factors, the consumer score and the high scorersscore.

According to an aspect, the user interface module is further configuredto provide, to the consumer, for each of the plurality of score factors,an indication of whether the score factor positively or negativelyimpacts a credit score of the consumer.

According to another aspect, determining a high scorer score comprisesaveraging relevant credit information associated with the set of highscorers.

According to yet another aspect, the plurality of score factors includesat least one of a number of maxed-out credit cards, a number of publicrecords, a number of credit inquiries, an average credit card limit, anaverage age of accounts, a mortgage standing, a number of missedpayments, a number of open credit cards, number of installment loans, acredit-to-debt ratio, an oldest account age, and a credit file updatetime.

According to another aspect, the analysis module is further configuredto determine a particular demographic, wherein the set of high scorersis associated with the particular demographic.

According to yet another aspect, the analysis is displayed in a userinterface on a mobile computing device associated with the consumer.

According to another aspect, the set of high scorers is periodicallyre-determined.

In yet another embodiment, a non-transitory computer storage thatcomprises executable instructions configured to cause one or morecomputer processors to perform operations comprises: receiving firstconsumer credit information associated with a consumer, wherein thereceived consumer credit information comprises summary data associatedwith categories of credit information that impact a credit score of theconsumer; receiving second credit information relating to a plurality ofconsumers with credit scores above a predetermined threshold, whereinthe received second credit information comprises summary data of theplurality of consumers associated with the categories of creditinformation; and generating, for display on a touch sensitive computingdevice, a user interface comprising a plurality of panes associated withrespective categories of credit information, wherein each pane isconfigured to provide a comparison between the first consumer creditinformation associated with the respective category and the secondcredit information associated with the respective category.

According to an aspect, each pane is further configured to provide anindication of whether credit information of the consumer in a respectivecategory positively or negatively impacts the credit score of theconsumer.

According to another aspect, each of the plurality of consumers isassociated with a particular demographic population.

According to yet another aspect, the particular demographic populationincludes at least one of an age, a gender, an ethnicity, an employmentstatus, a geographic location, a net worth, and an income level.

According to another aspect, the particular demographic population isassociated with the consumer.

BRIEF DESCRIPTION OF THE DRAWINGS

The following aspects and many of the attendant advantages of thedisclosure will become more readily appreciated as the same becomebetter understood by reference to the following detailed description,when taken in conjunction with the accompanying drawings, wherein:

FIG. 1 is a block diagram of an illustrative network environment inwhich a credit data analysis system may operate, according to anembodiment of the present disclosure.

FIG. 2 is a data flow diagram depicting an illustrative operation of thecredit data analysis system, according to an embodiment of the presentdisclosure.

FIG. 3 is a flow diagram depicting an illustrative operation of thecredit data analysis system in which high scorer values are presented toa user, according to an embodiment of the present disclosure.

FIG. 4 is a flow diagram depicting an illustrative operation of thecredit data analysis system in which high scorer values are determined,according to an embodiment of the present disclosure.

FIGS. 5A and 5B are illustrative user interfaces of the credit dataanalysis system, according to embodiments of the present disclosure.

DETAILED DESCRIPTION

A computing device such as a mobile smart phone and/or a tablet maydisplay a score factors user interface comprising one or more creditscore factor user interface panes that provide context for a consumer'sand/or user's credit report, as illustrated in the examples of FIGS. 5Aand 5B. The score factor user interface pane may comprise a descriptionof the type of data to be displayed and/or compared that is associatedwith a summary of credit score inputs. The analysis of this summary ofcredit score inputs (e.g. sometimes known as a summary attribute) may beconsidered a score factor. Score factors are a type of summary data thatmay influence a consumer's score positively or negatively. For example,the user interface element 509 (in FIG. 5A) has the description of ascore factor called “You have 5 or more credit inquiries” which is basedon a summary attribute that counts the number of credit inquiries for aparticular consumer, and also determines an average number of creditinquiries for a group of high scorers. If the summary data indicatesthat there are 5 of more credit inquiries for a particular consumer,then the condition for that score factor is met, and the mobile devicemay display the score factor description.

In some embodiments, a score factor user interface pane may also have anindicator associated with whether the particular score factor affectsthe score positively or negatively. For example, score factor userinterface element 510 contains an arrow pointing at the bottom of thescreen (or may have a red color) to indicate that a higher numberaffects the consumer's credit score negatively. Whereas score userinterface element 504 contains an arrow pointing at the top of thescreen (or may have a green color) to indicate that a higher numberaffects the score positively. The categories for the score factors, andwhether a higher number is more positive or negative (e.g., a positivescore factor or a negative score factor) may be transmitted from acredit bureau data store, a credit bureau, and/or a back end system.

Also displayed within a score factor user interface pane are the valuesof the consumer's related score factor data and the average data pointsin the same score factor for a group of high scorers. For example, scorefactor user interface pane 510 for the score factor “You have 5 or moreinquiries” displays the value 6 for the consumer, and 2 for the highscorers side by side, so that the user can easily know their own valuefor that category, and optionally compare that value between theconsumer values and scores of high scorers (that can be used as positiveguidelines to improve the consumer's score). In general, a high scoreris a person who is considered low risk by, for example, banks and/orlenders, and/or who has an excellent credit score, for example, anoverall credit score between 726 and 830. In an embodiment, the term“high scorers” refers to a group of persons who each have excellentcredit scores.

Advantageously, the score factor user interface enables a user tocompare their own scores in various credit categories to scores of highscorers in the various credit categories. In an embodiment, a highscorers score in one of the various categories may be an average of allthe high scorers' individual scores in the category. Categories (alsoreferred to herein as credit categories or score factors) in which auser may compare their own score to a high scorers score may include,for example, a number of maxed-out credit cards, a number of publicrecords, a number of credit inquiries, an average credit card limit, anaverage age of accounts, a mortgage standing, and/or a number of missedpayments, among others.

In an embodiment, the credit data analysis system allows the user tocompare the user's scores in various categories with scores of highscorers derived from high scorers of a particular demographic and/orgeographic region. For example, a user may be primarily interested intheir scores in their own state. The credit data analysis system allowssuch a user to compare their scores in various credit categories to highscorers located only in their state. Thus, the user may advantageouslydetermine credit categories in which they excel, and in which they lack,as compared to a relevant population of high scorers.

In an embodiment, the scores of high scorers and/or the user's scores inthe various credit categories may periodically be updated. Such updatingmay be initiated automatically by the user, and/or it may occurautomatically.

A score factor user interface element may be selected via the touchscreen interface to reveal more information about the credit scorefactor. When touched, in some embodiments, the computing device maydisplay the “virtual” reverse side of the score factor, such as userinterface element (512). The virtual reverse side may compriseexplanatory text about how a consumer's score in that particular scorefactor may affect his overall credit score. Virtual reverse sides mayoptionally be color coded depending on whether a score factor ispositive or negative. If touched again, the user interface element mayreturn to the original side to show the score factor description,consumer values, and high scorers values.

When transitioning to or from the reverse side of the score factor userinterface pane, the user interface element may appear to flip or rotateeither horizontally or vertically on its center vertical or horizontalaxis respectively to the reverse side. In some embodiments, such arotation or flip may occur more than once. In some embodiments, arotation or flip may occur several times in succession, where therotation speed slows down over time until the card comes to rest on thereverse side.

The score factor panes displayed on the user device may be selected bythe user, the computing device, the credit bureau, and/or other backendsystem such as a credit data analysis server based on which scorefactors apply to a particular user's credit information. For example,the user interface pane 510 has the description “You have 5 or moreinquiries”. This particular score factor may not be selected for displayif the number of credit inquiries for the consumer was less than 5.Additionally, the order that the user interface panes are displayed mayindicate the relative impact or importance of each score factor indetermining a consumer's credit score.

In some embodiments, when a score factor user interface pane is touched,the score factor instead displays to a user the specific creditinformation inputs that made up the score factor statistic. For example,if the user touches the user interface 502, the user device may displayinformation about the associated credit card accounts.

As used herein, the terms “user,” “individual,” and/or “consumer” may beused interchangeably, and should be interpreted to include users,applicants, customers, single individuals as well as groups ofindividuals, such as, for example, families, married couples or domesticpartners, and business entities. More particularly, the terms “user,”“individual,” and/or “consumer” may refer to: an individual subject ofthe financial services portal system (for example, an individual personwhose financial status and experience are being determined). In general,for the sake of clarity, the present disclosure usually uses the terms“consumer” and “user” to refer to an individual subject of the creditdata analysis system.

Embodiments of the disclosure will now be described with reference tothe accompanying figures, wherein like numerals refer to like elementsthroughout. The terminology used in the description presented herein isnot intended to be interpreted in any limited or restrictive manner,simply because it is being utilized in conjunction with a detaileddescription of certain specific embodiments of the disclosure.Furthermore, embodiments of the disclosure may include several novelfeatures, no single one of which is solely responsible for its desirableattributes or which is essential to practicing the embodiments of thedisclosure herein described.

System Overview

FIG. 1 is a block diagram of an illustrative network environment 100 inwhich a credit data analysis system may operate, according to anembodiment of the present disclosure. The credit data analysis systemmay include a credit data analysis server 101, a network 108, a user103, and a credit bureau data store 124. The constituents of the networkenvironment 100 may be in communication with each other either locally,or over the network 108.

Additionally, the credit data analysis server 101 may include a userinterface module 102, a data collection module 104, an analysis module106, a central processing unit (CPU) 114, a memory 116, a mass storagedevice 118, I/O devices and interfaces 120, and multimedia devices 122,all of which may communicate with one another by way of a communicationbus. The credit data analysis server 101 may include an arrangement ofcomputer hardware and software elements that may be used to implementthe credit data analysis system. FIG. 1 depicts a general architectureof the credit data analysis server 101, but the credit data analysisserver 101 may include more (or fewer) components than those shown inFIG. 1.

The user 103 may communicate with the network 108 through any type ofcomputing device capable of sending and receiving data to and from thecredit data analysis server 101. In an embodiment, the computing deviceoperated by the user 103, or with which the user 103 interacts, may be amobile computing device, may include a web browser configured tocommunicate with the user interface module 102, and/or may be capable ofrunning mobile applications that may communicate with the credit dataanalysis server 101. In an embodiment, more than one consumer mayinteract with the credit data analysis server 101. For example, manyusers may simultaneously (or substantially simultaneously) interactswith the credit data analysis server 101, making requests and receivingresponses.

The network 108 may be any wired network, wireless network, orcombination thereof. In addition, the network 108 may be a personal areanetwork, local area network, wide area network, cable network, satellitenetwork, cellular telephone network, or combination thereof. Protocolsand components for communicating via the Internet or any of the otheraforementioned types of communication networks are well known to thoseskilled in the art of computer communications and thus, need not bedescribed in more detail herein.

The credit data analysis server 101 is a computing device that mayperform a variety of tasks to implement the credit data analysis system,and may include hardware such as processors, memory, storage media,network interfaces, and so on. The operating of the credit data analysisserver 101 may be implemented through, for example, the user interfacemodule 102, the data collection module 104, and the analysis module 106.The modules of the credit data analysis server 101 may be stored insoftware or in read only memory or otherwise be accessible to thecomputing hardware of the financial portal.

In an embodiment, the user interface module 102 may enable credit dataanalysis server 101 to communicate via an HTTP or other networkcommunications protocol. In an embodiment, the user interface module 102is configured to serve one or more webpages to the user 103 thatconnects to the credit data analysis server 101. User interface module102 may also provide features such as data gathering from users,authentication, email communication, telephone and/or voice interfaces,and/or other services as may be used by credit data analysis server 101.The user interface module 102 may further generate user interfaces fordisplay to the user 103. Exemplary user interfaces generated by the userinterface module 102 are described in reference to FIGS. 5A and 5B.

User interface module 102 may include computer executable portions thatare executed by the credit data analysis server 101 and/or by a usercomputing device (such as the computing device 202 of FIG. 2). Thus,discussion herein of operations performed by the user interface module102 may be performed entirely by the credit data analysis server 101,entirely by the computing device 202, or some portions may be performedby the credit data analysis server 101 while other portions areperformed by the computing device 202. Furthermore, other computingsystems may also perform all or some of the processes discussed withreference to the user interface module 102.

In one embodiment, the user interface module 102 may access data fromdata collection module 104 or credit bureau data store 124, and use thatdata to construct user interfaces that assist the user in understandinghis or her credit score and how the underlying data is used to constructa credit score. Such information may be presented to the end user and isdesigned to be easily manipulated and/or understood by the user. In anembodiment, the user interfaces transmitted by user interface module 102are interactive. Various embodiments of the user interfaces that may beprovided by user interface module 102, including score factor userinterface panes that are shown and described throughout thisspecification.

User interface module 102 may be configured to construct user interfacesof various types. In an embodiment, user interface module 102 constructsweb pages to be displayed in a web browser or computer/mobileapplication. The web pages may, in an embodiment, be specific to a typeof device, such as a mobile device or a desktop web browser, to maximizeusability for the particular device. In an embodiment, user interfacemodule 102 may also interact with a client-side application, such as amobile phone application (an “app”) or a standalone desktop application,and provide data to the application as necessary to display underlyingcredit score information.

In an embodiment, the credit data analysis server 101 may furtherinclude data collection module 104. The data collection module 104 mayperform various tasks of gathering and/or collecting data for the creditdata analysis system. The data collection module 104 may provide aconsistent interface for external services and databases, such asfinancial services, credit bureau services, and the like, to interactwith the credit data analysis server 101. For example, the credit dataanalysis server 101 may retrieve credit data, including categories andattributes associated with the credit data, from the credit bureau datastore 124 via the data collection module 104. In an embodiment, the datacollection module 104 may include an application programming interface(API) that may enable the credit data analysis server 101 to receivedata from external services and databases, and may further enableexternal services and databases to retrieve data about a user from thecredit data analysis server 101. It may also enable external servicesand databases (such as the credit bureau data store 124) to provideinformation to the credit data analysis server 101, such as updatedcredit data (including related categories and attributes) related to theuser 103.

The credit bureau data store 124 may include information and datarelated to the credit of many individuals, including the user. In anembodiment, the credit bureau data store 124 may comprise one or morecredit bureaus and their databases, which usually receive informationfrom raw data sources, such as banks and creditors. In an embodiment,the credit bureau data store 124 is in communication with the creditdata analysis server 101 over the network 108. In an embodiment, thecredit bureau data store 124 is in communication with the credit dataanalysis server 101 over a dedicated and/or secure data channel. In anembodiment, the credit bureau data store 124 is operated by a creditbureau.

In an embodiment, credit data is gathered on demand as required by thecredit data analysis system. In another embodiment, credit data isgathered on a periodic basis independent of requests for informationfrom the credit data analysis server 101. In another embodiment, creditdata is stored on the credit data analysis system (for example, in aclient computing device and/or data collection module 104), in whichcase, retrieval of credit data from a credit bureau may not benecessary. The credit data may include a complete credit report about aconsumer, summary data such as credit attributes (also referred to ascredit variables) that are calculated using various modules, such asExperian's STAGG (standard aggregation variables), credit data inputs tocalculate a complete or partial credit score, credit card data, publicrecord data, credit inquiry data, bank account data, loan data, mortgagedata, line of credit data, payment data, and the like. Each credit datainput may be associated with a particular score factor. A score factoris a value that is known to impact credit score. Examples of scorefactors are described elsewhere herein. In some embodiments, the datacollection module 104 may calculate summary attributes (e.g. STAGGattributes) or perform other modifications on the credit report or othercredit data gathered, to determine a score factor. In some embodiments,a score factor value may be a summary or STAGG attribute value.

In an embodiment, the credit bureau data store 124 may be embodied inhard disk drives, solid state memories, and/or any other type ofnon-transitory, computer-readable storage medium remotely or locallyaccessible to the credit data analysis server 101. The credit bureaudata store 124 may also be distributed or partitioned across multiplestorage devices as is known in the art without departing from the spiritand scope of the present disclosure.

In an embodiment, the data collection module 104 may also gatherexplanatory text information about how a credit score is calculated.This may include description text, algorithms, formulas, executablecode, statistical variables, and the like. This information may be usedto understand the significance of a score factor in calculating a creditscore. This may include an indication of whether a higher or lower valueof a particular score factor positively or negatively impacts a creditscore. In an embodiment, the explanatory text and positive or negativeindications may be retrieved from the credit bureau data store 124 on anon-demand basis as needed by the credit score factor computing system.In another embodiment, the models and/or algorithms are retrieved on aperiodic basis. In another embodiment, the credit score factor computingsystem internally stores the models and/or algorithms (for example,stored on a client computing device).

In an embodiment, the analysis module 106 may enable the credit dataanalysis server 101 to determine credit scores, credit categories, scorefactors, demographic groups, geographic locations and/or regions, highscorers groups, and/or scores of high scorers, among others. Theanalysis module 106 may use data from the data collection module 104.Furthermore, the user interface module 102 may communicate with analysismodule 106 in order retrieve the various values, scores, and inputspreviously mentioned.

Multimedia devices 122 may include, for example, an optional displayand/or an optional input device. The optional display and optional inputdevice may be used in embodiments in which users interact directly withthe credit data analysis server 101. The I/O devices and interfaces 120may include a network interface (among other devices) that may providethe credit data analysis server 101 with connectivity to one or morenetworks or computing systems. For example, the network interface maycommunicate over the network 108 with the credit bureau data store 124,and/or the user 103. The CPU 114 may thus receive information andinstructions from other computing systems or services through a network.The CPU 114 may also communicate with memory 116, and further provideoutput information for the multimedia devices 122. The I/O devices andinterfaces 120 may accept input from the optional input device, such asa keyboard, mouse, digital pen, touch screen, or gestures recorded viamotion capture. The I/O devices and interfaces 120 may also output audiodata to speakers or headphones (not shown).

The memory 116 contains computer program instructions that the CPU 114executes in order to implement one or more embodiments of the creditdata analysis system. The memory 116 generally includes RAM, ROM and/orother persistent or non-transitory computer-readable storage media. Thememory 116 may store an operating system software (such as Windows XP,Windows Vista, Windows 7, Windows 8, Windows Server, Unix, Linux, SunOS,Solaris, Macintosh OS X, or other compatible and/or proprietaryoperating systems) that provides computer program instructions for useby the CPU 114 in the general administration and operation of the creditdata analysis server 101. The memory 116 may further include otherinformation for implementing aspects of the credit data analysis system.

For example, in one embodiment, the user interface module 102, the datacollection module 104, and/or the analysis module 106 may be implementedin the memory 116. The user interface module 102, the data collectionmodule 104, and the analysis module 106, as implemented in the memory116, may facilitate the same tasks as those described.

In an embodiment, the user interface module 102, the data collectionmodule 104, and/or the analysis module 106 may be stored in the massstorage device 118 as executable software codes that are executed by theCPU 114. The modules may include, by way of example, components, such assoftware components, object-oriented software components, classcomponents and task components, processes, functions, attributes,procedures, subroutines, segments of program code, drivers, firmware,microcode, circuitry, data, databases, data structures, tables, arrays,and variables.

In general, the word “module,” as used herein, refers to logic embodiedin hardware or firmware, or to a collection of software instructions,possibly having entry and exit points, written in a programminglanguage, such as, for example, Java, Lua, C or C++. A software modulemay be compiled and linked into an executable program, installed in adynamic link library, or may be written in an interpreted programminglanguage such as, for example, BASIC, Perl, or Python. It will beappreciated that software modules may be callable from other modules orfrom themselves, and/or may be invoked in response to detected events orinterrupts. Software modules configured for execution on computingdevices may be provided on a computer readable medium, such as a compactdisc, digital video disc, flash drive, or any other tangible medium.Such software code may be stored, partially or fully, on a memory deviceof the executing computing device, such as the credit data analysisserver 101, for execution by the computing device. Software instructionsmay be embedded in firmware, such as an EPROM. It will be furtherappreciated that hardware modules may be comprised of connected logicunits, such as gates and flip-flops, and/or may be comprised ofprogrammable units, such as programmable gate arrays or processors. Themodules described herein are preferably implemented as software modules,but may be represented in hardware or firmware. Generally, the modulesdescribed herein refer to logical modules that may be combined withother modules or divided into sub-modules despite their physicalorganization or storage.

In some embodiments, the functionality of the credit data analysisserver 101 may be implemented partially or entirely by a computingdevice and/or mobile computing device operated by, for example, the user103. Accordingly, the user computing device may include the userinterface module 102, the data collection module 104, the analysismodule 106, and/or other components that operate similarly to thecomponents illustrated as part of the credit data analysis server 101,including a CPU 114, network interface, mass storage device 118, I/Odevices and interfaces 120, memory 116, and so forth.

Many of the devices described herein are optional in variousembodiments, and embodiments of the credit data analysis system may ormay not combine devices. Moreover, any computing devices operated byuser 103 and/or the credit data analysis server 101, may each beembodied in a plurality of devices, each executing an instance of therespective devices. However, devices need not be distinct or discrete.Devices may also be reorganized in the credit data analysis system. Forexample, the credit data analysis server 101 may be represented in asingle physical server or, alternatively, may be split into multiplephysical servers. The entirety of the functions of the credit dataanalysis server 101 may be represented in a single user computing deviceas well. Additionally, it should be noted that in some embodiments, thefunctionality of the credit data analysis server 101 is provided by onemore virtual machines implemented in a hosted computing environment. Thehosted computing environment may include one or more rapidly provisionedand released computing resources, which computing resources may includecomputing, networking and/or storage devices. A hosted computingenvironment may also be referred to as a cloud computing environment.

The computing device and/or mobile computing device operated by the user103, and described above, may be any computing device capable ofcommunicating over the network 108, such as a laptop or tablet computer,personal computer, personal digital assistant (PDA), hybrid PDA/mobilephone, mobile phone, in-vehicle computer device or navigation system,global positioning system (GPS) device, electronic book reader, set-topbox, camera, audiobook player, digital media player, video game console,in-store kiosk, television, one or more processors, integratedcomponents for inclusion in computing devices, appliances, electronicdevices for inclusion in vehicles or machinery, gaming devices, or thelike.

High Level Data Flow

FIG. 2 is a data flow diagram depicting an illustrative operation of thecredit data analysis system, according to an embodiment of the presentdisclosure. The data flow of FIG. 2 illustrates an exemplary process foraccessing credit data of a particular user, accessing credit data ofhigh scorers, analyzing the accessed credit data to compare high scorersvalues and the users values in a number of credit categories (alsoreferred to as score factors), rendering the analyzed credit data withinuser interfaces so that consumers may better understand the impact oftheir credit data, and displaying flippable score factor cardsassociated with their credit data. Depending on the embodiment, theprocess illustrated by interactions 1-4 of FIG. 1 may include fewer oradditional interactions and/or the interactions may be performed in anorder different than is illustrated.

The exemplary data flow of FIG. 2 includes the credit data analysisserver 101, the credit bureau data store 124, the network 108 (depictedby the dotted line), the user 103, and a client computing device 202which may be operated by the user 103. The credit data analysis server101 further includes the user interface module 102, the data collectionmodule 104, and the analysis module 106, as described in reference toFIG. 1 above.

The computing device 202 may be an end user computing device thatcomprises one or more processors able to execute programmaticinstructions. Examples of such a computing device 202 are a desktopcomputer workstation, a smart phone such as the apple iPhone, a computerlaptop, a tablet PC such as the iPad, a video game console, or any otherdevice of a similar nature. In some embodiments, the computing device202 may comprise a touch screen that allows a user to communicate inputto the device using their finger(s) or a stylus on a display screen. Thecomputing device 202 (or any of the computing systems described herein,such as credit data analysis server 101), as described in reference toFIG. 1, may comprise storage systems such as a hard drive or memory, orcomprise any other non-transitory data storage medium. The storagesystems may be configured to store executable instructions that may beexecuted by one or more processors to perform computerized operations onthe client computing device, accept data input from a user (e.g. on thetouch screen), and/or provide output to a user using the display. Theseexecutable instructions may be transmitted to another device forexecution or processing by the device to implement the systems andmethods described herein. In an embodiment, the computing device 202 maycomprise software and/or hardware that implements the user interfacemodule 102. The computing device 202 may be in communication with thecredit data analysis server 101 and/or the credit bureau data store 124via the network 108.

The computing device 202 may also comprise one or more client programapplications, such as a mobile “app” (e.g. iPhone or Android app) thatmay be used by a consumer to understand their credit score, and initiatethe sending and receiving of messages in the credit data analysissystem. This app may be distributed (e.g. downloaded) over the networkto the client computing device directly from a credit bureau, from thecredit data analysis server 101, from data collection module 104, orfrom various third parties such as an apple iTunes repository. In someembodiments, the application may comprise a set of visual interfacesthat may comprise templates to display a consumer's credit datainformation from a credit report or associated attributes in scorefactor categories. In some embodiments, as described above, userinterfaces may be downloaded from another server or service, such as thecredit data analysis server 101. This may comprise downloading web pageor other HTTP/HTTPS data from a web server and rendering it through the“app”. In some embodiments, no special “app” need be downloaded and theentire interface may be transmitted from a remote Internet server tocomputing device 202, such as transmission from a web server that is apart of the credit data analysis server 101 to an iPad, and renderedwithin the iPad's browser.

Beginning with interaction (1), the computing device 202 may transmit tocredit data analysis server 101 a request for credit data (via the userinterface module 102). The requested credit data may include items suchas a score factors user interface including one or more score factoruser interface panes with credit categories scores related to the user103 and high scorers, that may be generated based on underlying creditdata. Such underlying credit data may include a score factor, creditreport, credit score, credit attributes, and/or explanatory informationregarding how attributes are calculated based underlying credit dataand/or how attributes impact the credit score. In some embodiments,attributes that summarize credit data (e.g. summary attributes orsummary credit information) fitting a particular category may beconsidered a score factor. The request may also include a request for anindication of whether a particular score factor (e.g. credit attribute)positively or negatively affects credit score.

The request may also comprise a request for high scorers information.High scorers information may comprise average summary data, such asattributes matching a score factor, that are calculated by averagingdata in that credit category from a group of high scorers. A high scorermay be considered a consumer that has a credit score above a certainthreshold and/or has some other attributes that are envied by a typicalconsumer. High scorers data may be associated with a particulardemographic group, such as a geographic area, and may summarize averagecredit data for high scorers within the demographic group.

In some embodiments, such a request may be accompanied with anauthentication or authorization request. For example, in someembodiments, access to credit data may be restricted based on useridentification. An authentication scheme may comprise submitting a username and password to the credit data analysis server 101, or any otherauthentication mechanism known by those skilled in the art. Theauthentication request may have occurred prior to the request for dataaccess and/or during the request. In some embodiments, although a usermay authenticate, only certain users will be authorized to receivecredit report data. For example, the credit data analysis server 101 maycomprise memory storing a list of users or types of users that may gainaccess to their credit data, such as paying users. In some embodiments,no authentication is necessary and credit data may be freely accessed byall users. Such a request may also include a request for the algorithmsor user interfaces that may be used by an “app” or browser to render andinteract with the requested credit data.

In some embodiments, some functionality may be accessible byunauthenticated users, and other functionality only accessible toauthenticated users. The authenticated and unauthenticated sections mayhave the same features, similar features, or different features. In anembodiment, the authenticated section offers additional features notavailable in the unauthenticated section. For example, credit data orcredit-related information is used in the various systems and methodsdescribed herein. This information may be stored in member accounts orautomatically retrieved based on member account data. In such anembodiment, the credit-related information may be automaticallypre-populated, so that members need not enter that information, whileunauthenticated users would enter their information manually.

In interaction (2) of FIG. 2, the credit data analysis server 101, viathe data collection module 104, may retrieve the requested information,and/or calculate the requested information, from the credit bureau datastore 124 (whether maintained by a credit bureau or another entityauthorized to provide credit data). In some embodiments, the datacollection module 104, upon receiving a request from the computingdevice 202, may retrieve or calculate a credit report, credit score,attributes, explanatory data, and/or high scorers data from its localstorage and fulfill the access request without consulting a creditbureau.

For example, the data collection module 104 may have previously receiveda credit report and credit score from a credit bureau for that user andwould have the report cached in its local storage. Alternatively,previously retrieved and/or calculated scores of high scorers and datamay be stored locally. In some embodiments, the credit report and scoremay be periodically retrieved for users from a credit bureau in order tohave it locally on file. Alternatively, or in combination, the datacollection module 104 may retrieve in real time the credit report,credit score, high scorers data, and/or summary credit attributes fromthe credit bureau data store 124. Any credit information required in thevarious embodiments, such as explanatory information, information abouthow a credit score is calculated, summary data, credit reports, creditscore, high scorers data, etc., may be retrieved periodically and ondemand, or cached in this manner.

The data collection module 104 may also calculate any attributesrequired by the user interfaces implemented by the user interface module102 (if any are required outside of default summary attributes). Forexample, summary credit attributes may be calculated by credit bureausthat summarize credit data. These summary credit attributes can berequested along with, or as an alternate to, a credit report or creditscore. However, the data collection module 104 may also compile thesummary attributes based on the credit report, or calculate customattributes based on the credit report. For example, one summaryattribute may comprise a calculation of the amount of available creditfor a consumer. Such an attribute may be calculated based upon summingup all of the un-used credit available in a consumer's accounts. Thisfinal figure may then be associated with the credit report and stored inthe data gathering module for later transfer to a client computingdevice 202. These calculations may be performed on demand orperiodically. In an embodiment, attribute calculation and/or creditcategory calculations may be performed by the analysis module 106.

In addition to retrieving credit reports, scores, and/or attributes, thedata collection module 104 may, in some embodiments, retrieveexplanatory text about how the attributes involved may impact a creditscore, and indications of whether a high or low value in an attributemay impact a credit score. These may be stored locally on disk withinthe data collection module 104, or retrieved from credit bureau datastore 124 and/or other credit database. For example, explanatory textand/or indicators may be retrieved from the credit bureau data store 124and/or known in advance by the data collection module 104. Based on theretrieved information, the data collection module 104 and/or the userinterface module 102 may alter or generate a score factors userinterface (and/or other user interface) to reflect this information. Forexample, as shown in FIG. 5A, one summary attribute may be how manymaxed out credit cards a consumer has 502. Explanatory text, such as thetext that appears in pane 512 may be used in a score factors userinterface.

After retrieving and/or calculating the information, interaction (3) ofFIG. 2 illustrates the retrieved and/or calculated data being providedto the analysis module 106 such that the retrieved and/or calculateddata may be parsed, further calculations may be performed, and/or highscorers information may be determined. In an embodiment, a high scoreris a person who is considered low risk by, for example, banks and/orlenders, and/or who has an excellent credit score, for example, anoverall credit score between 726 and 830. A high scorer may also bereferred to as a score master and/or an expert. In an embodiment, theterm “high scorers” may refer to a group of persons who each haveexcellent credit scores. Thus, in some embodiments, a group of highscorers is determined by the analysis module 106, and score factorsand/or attributes in a number of credit categories for the high scorersare determined. In an embodiment, the high scorers group may bedetermined based on some demographic criteria, such as a geographiclocation. In some embodiments, the high scorers group that is used incomparison to a particular consumer may be determined by a particularconsumer. For example, a particular consumer may want to be compared toa group of individuals in the consumer's ZIP Code that all have creditscores above 800, while another consumer may want to be compared to agroup of individuals in the consumer's ZIP Code that all have creditscores between 600 and 700. The process of determining scores of highscorers is described further in reference to FIGS. 3 and 4 below.Additionally, analysis module 106 determines scores and/or attributesassociated with the user 103 in a number of credit categories. Thedetermined scores and other analyzed data is then transmitted back tothe user interface module 102.

In an embodiment, the analysis module 106 may store the received creditinformation, and parse the credit report, credit score, attributes,explanatory text, indications, or high scorers data that may be requiredto render the user interface in various embodiments. This may includeorganizing in a data structure one or more received attributes and otherreceived information into such as explanatory text and indications byassociation. For example, the credit data analysis system may matchappropriate description text, explanatory text, attribute and/orcategory values (for the user and the high scorers), and indicationstogether. The system may be pre-programmed to recognize certainattributes as information for score factor categories to be used, andprepare the data structures appropriately. In some embodiments, thereceived information will also indicate which attributes to use andwhich score factor user interfaces to show, based on a selection ofscore factors made by the credit data analysis server 101 or a creditbureau. Any additional attributes or summarization data may becalculated if needed based on the credit report or accompanyinginformation for use in the user interfaces.

In interaction (4) of FIG. 2, a user interface including the user'scredit data and high scorers credit data in each of multiple categoriesis transmitted to the computing device 202, via the network 108. Thisinformation may be transmitted using a text credit report format, an XMLformat, using web services APIs, or any other organized data structureor protocol for transferring the information between the credit dataanalysis server 101 and the computing device 202. Alternatively, thisinformation may be transmitted to the computing device 202 as a part ofa web page and accompanying web page user interfaces to be rendered withan app or a browser, such as software code configured to generate thevarious visual features of the credit summary user interface discussedherein. In this embodiment, the credit data analysis system may act as aweb page or web site configured to provide static, scriptable, orexecutable code and data that may be used to implement the entireinvention, even with a computing device 202 only capable of webbrowsing. In an embodiment, the user interface module 102 is located inthe computing device 202, and thus only the credit data is transmittedto the computing device 202 where a user interface is provided.

In an embodiment, the computing device 202 displays a score factors userinterface (also known as a flippable score factor pane user interface),where each score factor pane is based upon a selected summary or customattribute, that may use associated explanatory text, positive ornegative indicators, a short description, a display of the attribute'svalue, and high scorers information. By way of example, FIGS. 5A and 5Billustrate score factor user interfaces that may be used in someembodiments.

Optionally, in some embodiments, the summarized data/score factorinformation displayed in each score factor user interface pane may belinkable to a displayable portion of a credit report on the computingdevice 202. For example, by touching a specific piece of data within ascore factor user interface pane or the score factors user interface,the user may be automatically directed to a portion of the user's creditreport displaying detailed information related to the score factor. Withreference to FIG. 5A, for example, if the text “You do not have anymaxed-out credit cards” 502 was touched, the computing device 202 and/orcredit data analysis system may direct the user to a portion of theircredit report listing all credit card account information, includingeach individual credit limit for each account and/or other data relatedto the user's total credit limit. Advantageously, this allows a user toeasily browse and visualize a high level overview of their credit dataand drill down into their detailed credit report for furtherinformation. Alternatively, upon touching the text, the user may bedirected to an indication of the source of the high scorers score, forexample, the attributes associated with the members of the determinedhigh scorers group.

Credit Reports and Credit Bureaus

The credit data analysis system may be separate from a credit bureau orcredit bureau data store 124. One of the purposes of the credit dataanalysis system is to interface with the credit bureau or any databasethat has data that will eventually be used in a user interface bycomputing device 202. The credit data analysis system may request andextract the appropriate credit data for a specific consumer based on auser using the computing device 202. This allows for a single point ofcontact for computing device 202 interaction. The credit data analysissystem can then be configured to request from and receive data fromcredit bureaus or other credit databases.

Alternatively, the credit data analysis system may be executed by acredit bureau itself. In this case, the credit report system and thecredit bureau functionality may be combined, with no need to transferdata over a wide area network between them. In some embodiments, theclient computing device 202 may be configured to interact directly witha credit bureau over a network, to access a credit report and summaryattributes. In this case, any custom attribute creation or processingneeded must be performed by the computing device 202.

Example Method of Providing High Scorers Values to a User

FIG. 3 is a flow diagram depicting an illustrative operation of thecredit data analysis system in which high scorers values are presentedto a user, according to an embodiment of the present disclosure. Invarious embodiments, fewer blocks or additional blocks may be includedin the process, or various blocks may be performed in an order differentfrom that shown in FIG. 3. In particular, the blocks in FIG. 3 may beperformed by computing device 202 and/or credit data analysis server 101(or any combination thereof), depending on which computingdevice/software service has access to the required credit data.

In general, credit bureaus make their data available to consumers andbusinesses, usually for (but not limited to) the purpose of checking aconsumer's credit history and credit score. A credit bureau's creditreport may include, among other things, data concerning payment history(such as current accounts and late payments), credit usage andavailability, the age of financial accounts, the types of financialaccounts, and inquiries into credit reports or credit scores. This datamay be collected from one or more raw data sources which may compriseinformation from consumers' banks, mortgagors, lenders, creditors,services, utilities, public records, and other institutions where aconsumer holds a financial account. The data may include a status ofeach account, such as when the last bill was paid, how late a recentpayment is or how behind a consumer is on their account, a paymenthistory, the available credit allowed in an account, the accountbalance, and when an account was opened and/or closed, among othercredit information.

Beginning at block 302, a request is received from the user or consumerfor consumer credit information. The request may specify, for example,that the user would like to view credit information and comparison ofthe consumers credit information to a group of high scorers, such as inthe sample user interface of FIG. 5A. The request may be transmitted bythe computing device 202 to the credit data analysis server 101, forexample. The request may be issued by sending it over an electronic widearea network, such as the Internet. The credit bureau receives thisrequest, and may, if necessary, charge and/or authenticate the requestorby methods known in the art.

Then, at block 304, the credit data analysis server 101 accessesconsumer credit data associated with the user's request. For example,the accessed data may include credit data, a credit report, and/orassociated attributes of the consumer, as well as similar data for oneor more high scorers. The data may be retrieved from the credit bureaudata store 124 and/or or a credit bureau by the data collection module104, for example. The credit data provided to the data collection module104 may, in an embodiment, comprise data and/or information precollectedfrom raw data sources.

Also in block 304, the credit data analysis system may, in anembodiment, either access or retrieve cached, precalculated, and/orprecompiled credit data specific to a consumer, such as a credit report,score, attributes about the consumer, score factors that apply specificto the consumer, explanatory text related to each attribute/scorefactor, a positive or negative indication for each score factor. Forexample, based on information periodically collected by the creditbureau from raw data sources disclosed above, the credit bureau may havepre-compiled credit information into a credit report and other relatedcredit information in advance.

Alternatively, this information may be determined based on informationaccessed and compiled in block 304. For example, in some embodiments,the credit bureau and/or the data collection module 104 may use theaccessed credit information to calculate a credit score usually based ona proprietary formula. The credit bureau may also calculate and/orcreate the attributes that are often associated with a credit report.These attributes may be summary variables/attributes (that maycorrespond to a score factor) that summarize data related to individualaccounts. For example, one STAGG attribute (an example type of summaryattribute) may be a calculation of the total max credit for all creditcard accounts, which may correspond to a score factor. A positive ornegative indication, or explanatory text, of each score factor may bedetermined or accessed based on how the formula uses the score factor todetermine its credit score.

Next, at optional block 306, the credit data analysis system mayoptionally determine a high scorers population of interest. In anembodiment, the user 103 may specify, through the computing device 202,a particular demographic of interest to the user. Such as specificationmay be included in the request provided to the credit data analysissystem. For example, the user 103 may only be interested in comparingtheir own credit scores and/or attributes to others that are located ina similar geographic region. In another example, the user 103 may onlybe interested in a comparison with others having a similar income levelto the user. Examples of demographics that may be specified may include,but are not limited to, gender, ethnicity, employment status, geographiclocation, net worth, and income level, among others. Examples ofgeographic locations that may be specified may include, but are notlimited to, a neighborhood, a city, a county, a state, and a country. Inan embodiment, one or more demographics may be specified. In anembodiment, the demographic specified may be associated with the usermanually and/or automatically. For example, the credit data analysissystem may automatically determine (based on the user's credit data, forexample), the user's gender, address, and/or income level, among otherexamples. The system may then automatically specify the relevantdemographic, for example, the population of individuals in the user'shome state.

At block 308, high scorers values are determined and/or accessed (ifthey were previously determined). Analysis module 106 may determine highscorers values for relevant demographics (if a demographic populationswas specified) using the data retrieved and/or accessed in block 304. Asmentioned above, in general, a high scorer is a person who is consideredlow risk by, for example, banks and/or lenders, and who has an excellentcredit score, for example, an overall credit score between 726 and 830.In an embodiment, the term “high scorers” refers to a group of personswho each have excellent credit scores. In another embodiment, “highscorers” may refer to a group of persons that, taken together, onaverage have an excellent credit score. In an embodiment, the group ofhigh scorers may be limited to individuals having associatedcharacteristics that fall within the specified demographic (as describedabove).

In some embodiments, one or more high scorer's credit statistics may beused to identify characteristics about good credit scores. A high scoreris a broad term, but may refer to a member of the high scorers groupthat is comprised of a group of consumers that have high credit scores.For example, a threshold credit score such as 726 or above may beselected as a high scorer credit score by an administrator and/orautomatically by the credit data analysis system. If a consumer's scoreis 726 or above, he or she may be considered a member of the highscorers set. Additional factors may also be used to determine if aconsumer is a member of the high scorers set, such as whether a consumeris considered low risk by lenders. Additionally, as described above, inthe instance in which a demographic population is provided, only thosehigh scorers having the relevant characteristics may be considered partof the high scorers set. In an embodiment, the threshold credit scoremay be predetermined by the credit data analysis system, a creditbureau, and/or a user of the credit data analysis system (such as theuser 103). In an embodiment, the threshold credit score may be 850, 840,830, 820, 810, 800, 790, 780, 760, 750, 740, 730, 720, 710, 700, 690,680, 660, 650, 640, 630, 620, 610, 600, 590, 550, and/or any othercredit score.

Once the high scorers group or set is determined, in some embodimentsthe average inputs for a high scorer that are useful for comparison arecalculated. For example, some embodiments may determine the averagemaxed-out credit cards for a high scorer, the average mortgages in goodstanding for a high scorer, the average amount of public recordsattributed to a high scorer, the average age of accounts for a highscorer, the average payments missed, the average number of creditinquiries per month, among others. These averages may be based on mean,median, or mode or other complex criteria used to determine a typicalvalue for a member of the high scorers set. The calculated values may beaveraged of the entire group of high scorers, for example.

These average values, once calculated, may be used for comparison to aconsumer's credit score, such as the credit score of the data collectionmodule 104. For example, if the data collection module 104 has sixcredit inquiries and a high scorer has typically two credit inquires,showing this comparison to a user may give the user the idea to lowertheir credit inquiries so that their credit score inputs align moreclosely to a high scorers, resulting in a potentially higher creditscore. In as embodiment, credit scores or score factors are calculatedfor a number of different credit categories which are described below inreference to FIGS. 5A and 5B. While averages of credit attributes ofhigh scorers are discussed herein, in other embodiments other (oradditional) mathematical operations may be applied to aggregate dataassociated with multiple high scorers, such as an arithmetic mean,median, mode, standard deviation, range, etc., of a group of consumersthat meet the high scorers criteria (whether default criteria set by thesystem or custom criteria set by a consumer).

Comparison either to the threshold credit score for a high scorer, orcomparison of the inputs to high scorers can be used to determinewhether a user of the simulation or visualization of credit data is ontrack to be a high scorer, or is already a high scorer. For example,having a number of credit score inputs that are better than a highscorer's input, such as having one credit inquiry per month whereas theaverage for high scorer's is two, may determine whether a user should begiven a special status, such as the title high scorer, or receive aprogress indicator or badge indicators that show high scorer status forall, one, or some credit score input categories. Examples of high scorercomparisons may be seen in FIGS. 5A and 5B, which are described below.

In an embodiment, high scorers information is provided from the creditbureau data store 124. For example, high scorers values may beprecalculated by the credit bureau and stored in the credit bureau datastore 124, where they may be accessed by the data collection module 104.In an embodiment, high scorers values may be cached by the credit dataanalysis server 101 for rapid reuse.

At block 310, the complied information, including the consumer creditinformation and scores, and the high scorers values and scores may betransmitted to the computing device 202 for display in a user interfaceto the user 103. In other embodiments, the actual credit data may not betransmitted to the computing device 202 and, rather, software code(e.g., HTML, Java, Perl, Ruby, Python, etc.) may be transmitted to thecomputing device 202. For example, code that is usable by the computingdevice 202 to render the user interface may be transmitted, withouttransmitting a data structure that separately includes the actual creditdata. In some embodiments, the system distinguishes between the initialtransmission of credit data required for user interfaces, and subsequenttransmissions of user interface data so that it may transmit onlyportions that are necessary to update a score factor user interface fornew credit data. This may be done, for example, using an XMLHttpRequest(XHR) mechanism, a data push interface, or other communicationprotocols.

FIG. 4 is a flow diagram depicting an illustrative operation of thecredit data analysis system in which high scorer values are determined,according to an embodiment of the present disclosure. In variousembodiments, fewer blocks or additional blocks may be included in theprocess, or various blocks may be performed in an order different fromthat shown in FIG. 4. In particular, the blocks in FIG. 4 may beperformed by computing device 202 and/or credit data analysis server 101(or any combination thereof), depending on which computingdevice/software service has access to the required credit data. Theprocess of FIG. 4 may be performed, for example in blocks 304-308 ofFIG. 3.

Starting at block 402, consumer credit data is accessed by the creditdata analysis system in substantially the same way as described inreference to block 304 of FIG. 3. Then, at optional block 404, if one ormore demographic populations have been specified (as described inreference to block 306 of FIG. 3), the accessed credit data is organizedaccording to the demographic. Thus, for example, if the user hasspecified a geographic location including the State of California,credit data that falls into that geographic location will be assembled.

Next, at block 406, the high scorers group or set members aredetermined. This step is accomplished substantially as described inreference to block 308 of FIG. 3. In general, only those individualsconsidered to have excellent credit, and/or to be low risk, aredetermined to be high scorers. The high scorers group may be furtherdefined by any demographic specification that may have optionally beenprovided. Continuing with the example above, only individuals who, forexample, reside in California may be selected by the credit dataanalysis system.

At block 408, for each of the credit categories (described above and asfurther listed and described below) scores of high scorers arecalculated from the determined group of high scorers.

In an embodiment, scores of high scorers may periodically be updated, asindicated by the arrow 410. Periodically updating the scores of highscorers provides the user with up-to-date comparisons between their owncredit scores and the archetypal scores of high scorers. In anembodiment, the user's scores may also be updated periodically. In anembodiment, the high scorers and/or users scores are updated yearly,quarterly, monthly, weekly, and/or daily, among other time periods.

In an embodiment, credit categories (and/or score factors) may bepredetermined by the credit bureau and/or the credit data analysissystem. Alternatively, credit categories (and/or score factors) may bedetermined by the credit data analysis server 101 during thedetermination of the user's credit scores. For example, the credit dataanalysis server 101 may determine relevant credit categories based onthe specified demographic population, and/or the user's credit data. Inan embodiment, more or fewer of the credit categories for which scoresare calculated may be displayed to the user.

Example User Interfaces

FIGS. 5A and 5B are illustrative user interfaces of the credit dataanalysis system, according to embodiments of the present disclosure. Theuser interfaces may be referred to as score factors user interfaces. Theuser interfaces include various user interface controls within the scorefactors user interface, such as score factor user interface panes. Invarious embodiments, the user interfaces shown in FIGS. 5A and 5B may bepresented as a web page, as a mobile application, as a stand-aloneapplication, or by other communication means. For example, theinterfaces may be displayed on the computing device 202 (FIG. 5A). Inother embodiments, analogous interfaces may be presented using audio orother forms of communication. In an embodiment, the interfaces shown inFIGS. 5A and 5B are configured to be interactive and respond to varioususer interactions. Such user interactions may include clicks with amouse, typing with a keyboard, touches and/or gestures on a touchscreen, voice commands, and/or the like. As one skilled in the art wouldrecognize, the systems and methods described herein are compatible withvarious types of input in addition to, or as a replacement for, thetouch screen input described.

As described above, FIGS. 5A and 5B illustrate two alternative samplescore factors user interfaces (500 and 530) for a specific individualconsumer (who in some embodiments may be the user of the clientcomputing device 202). The embodiments of FIGS. 5A and 5B showalternative layouts, which may include listing more or fewer scorefactors. Various summary attributes/score factors and other data relatedmay be displayed in addition to score factor user interface panes. Forexample, an area of the user interface may display high scorerinformation 501 (FIG. 5A), and/or the total number of score factorsdisplayed 534, including the number of helping score factors affectingthe specific consumer's score and the number of hurting score factorsaffecting the specific consumer's score (FIG. 5B). In addition, the userinterface may display the consumer's credit score 503 and 532, includinga grading of the value of the credit score (such as high, medium, lowrisk, etc.), and how up-to-date the credit report data being used isregarding this consumer (e.g., how recently the consumer's credit datawas downloaded from credit data analysis system or credit bureau). Asdescribed above, the factors may be listed by whether they are helpingfactors or hurting factors 536 (FIG. 5B), or they may be listed in someothers assortment (FIG. 5A). In addition, a visual indicator may beassociated with helping or hurting factors, such as an up or green colorarrow for a helping factor 504, or a down or red color arrow for ahurting factor 510. The user interface may also include the score factorpanes such as 502 and 509 (front side displayed), and 512 (reverse sidedisplayed), which may be manipulated as described above.

In this embodiment, high scorers values are displayed next to the user'scredit values for each credit category (or score factor). For example,in FIG. 5A the maxed-out credit cards pane 502 indicates the user'sscore 506 is zero, while the high scorers score 508 is also zero. Thus,because the user's score is the same as the high scorers score, thisscore factor is contributing positively to the user's overall creditscore, and a green up arrow 504 is displayed. In another example, thecredit inquiries pane 509 indicates that the user has more creditinquiries than the high scorers, and thus a red down arrow 510 isdisplayed because this score factor is contributing negatively to theuser's overall credit score. In other embodiment, other indicators mayindicate whether a score factor is helping or hurting the user's overallcredit score. For example, the user interface may include other coloredelements (such as colored text) and/or graphics or icons, among otherpossibilities.

In some embodiments, areas within the score factor user interface panesmay be functionally linked to detailed explanatory information and/orinformation in a consumer's credit report. For example, in FIG. 5A, theuser may select the arrow 514 to view additional explanatory informationrelated to that score factor. In another example, for the score factor“You do not have any maxed-out credit cards” 502 displayed in FIG. 5A,the “0” attribute under the “You” text may be linkable to explanatoryinformation and/or more detailed information in the consumer's creditreport. By touching or clicking on the “You” or “0” (or any otherappropriate related area), a user may be redirected to a second userinterface such as the one shown at 512, or to one that shows aconsumer's credit accounts, including the consumer's credit cards thatare not maxed out under the score factor. In this manner, a user may beable to dive straight into the credit report data that is impactingtheir credit score in a way described by the score factor.

Score Factor/Summary Data Categories

Score factors (or credit categories) may cover a variety of summarycredit categories that affect a user's credit score. For example, scorefactors may include, but are not limited to, those listed in the tablebelow.

Positive/ Negative Impact on Score Factor Condition/Category of SummaryCredit Score Information (“you” refers to a specific consumer) NegativeYou have one or more missed payments. Negative You have one or morePublic Records. Negative Your average age of accounts is less than 5years and 11 months. Negative You have five or more inquiries. NegativeYou do not have any open credit cards. Negative Your average credit cardlimit is less than $2,000. Negative You have three or more installmentloans. Negative You are currently behind on your mortgage. Negative Youhave one or more credit cards maxed out. Negative Your credit-to-debtratio is more than 51%. Negative You do not have a mortgage account ingood standing. Negative Your oldest account is less than six months old.Negative Your credit file hasn't been updated in six months. PositiveYou have never missed a payment. Positive You do not have any PublicRecords. Positive Your average age of accounts is more than 7 years and8 months. Positive You have less than two inquiries. Positive You havetwo or more open credit cards. Positive Your average credit card limitis more than $5,000. Positive You have no installment loans. PositiveYour mortgage is in good standing. Positive You do not have anymaxed-out credit cards. Positive Your Credit Used % is less than 16%.

In other embodiments, more or fewer score factors may be used. Thecondition levels may be different in other embodiments. For example, thecondition level for a positive impact on a consumer's credit scoresbased on credit used may be 10%, rather than the 16% listed in the tableabove.

Each of these score factors may have associated explanatory text thatmay be viewed in order to reveal more information related to that scorefactor, such as how the score factor condition is affecting a user'scredit score. In some embodiments, the score factor explanatory text mayalso display a measure of the impact of this particular score factor onyour credit score. For example, it may show that, absent this scorefactor applying to the consumer's credit score, the consumer's creditscore would have gone up or done by a certain number of points.

Score Factor Badges and Rewards

In some embodiments, badges may be awarded to a consumer based on theconsumer meeting or exceeding the average high scorers score in thescore factor categories. Such score badges may be displayed on eachscore factor user interface pane in the form of an icon. The icon mayappear on the score factor user interface pane when the consumer has metscore factor related conditions

For example, in some embodiments, a consumer may receive a certain badgeappearing on a score factor when they meet a preset threshold associatedwith the score factor value. This preset threshold may correspond to theconditional value required to display the score factor, or, this presetthreshold may correspond to a different threshold value. The presetthreshold may also correspond to meeting or exceeding a high scorervalue. In some embodiments, more than one badge may be associated with ascore factor, where each badge corresponds to a different threshold. Forexample, some score factors may have gold, silver, and bronze badges,where the gold badge may correspond to a score factor threshold valuethat will give the best effect on the credit score, and the silver andbronze badges correspond to thresholds of lesser positive impact oncredit score.

The badges for a consumer may be publicized to a user's Facebook accountor twitter (or any other social media or website) via applicationprogram interfaces for automatically sending and posting data to thosesources, among other methods. In addition, some embodiments may providean overall “high scorer” status based upon meeting the high scorerthresholds for a set number of score factors, or reaching a certainlevel of credit score. Some embodiments may also include configurablenotifications (SMS, text, email, sound, phone) when awarded a scorefactor badge or otherwise reaching a score factor threshold.

In some embodiments, badges need not be used, but any reward mechanismmay be used to signify to a consumer or the public that the consumer hasmet predetermined thresholds related to score factor conditions. Forexample, instead of earning badges, a consumer may receive giftcertificates, special promotions and coupons, ribbons, digital propertyin games, etc.

The badges may be calculated and tracked either on the computing device202, or by a credit bureau or the credit data analysis system, andtransferred to other computing systems such as Facebook via electroniccommunication over network 108 for additional display.

In an embodiment, the credit data analysis system may, instead ofidentifying high scorers and providing comparisons between theconsumer's score and scores of high scorers, identify low scorers andprovide comparisons between the consumer's score and scores of lowscorers. For example, the credit data analysis system may identify oneor more low scorers as individuals having credit scores below aparticular threshold. The credit data analysis system may next determinescores of the low scorers in one or more of various credit categories(in a similar manner as described above in reference to high scorers).Then the credit data analysis system may display the consumer's scoresnext to the scores of the low scorers for a comparison. In anembodiment, the credit data analysis system may indicate categories(e.g., score factors) in which the consumer's scores are similar toscores of the low scorers, and that are thus hurting the consumer'soverall credit score. Similarly, the credit data analysis system mayindicate categories (e.g., score factors) in which the consumer's scoresare different from scores of the low scorers, and that may not beaffecting or helping the consumer's overall credit score. In variousembodiment, the credit data analysis system may display the consumer'sscores across any number of score factors in comparison scores of highscorers, low scorers, average scorers (e.g., individuals having averagescores), and/or any other scorers group along the spectrum from high tolow.

Advantageously, the credit data analysis system and the score factoruser interface enables a user to compare their own scores in variouscredit categories to scores of high scorers in the various creditcategories. In an embodiment, the credit data analysis systemadvantageously allows the user to compare the user's scores in variouscategories with scores of high scorers derived from high scorers of aparticular demographic and/or geographic region. Thus, the user mayadvantageously determine credit categories in which they excel, and inwhich they lack, as compared to a relevant population of high scorers.Further, scores of high scorers may advantageously be updatedperiodically, thus providing the user of the credit data analysis systemwith constantly up-to-date score comparisons.

Depending on the embodiment, certain acts, events, or functions of anyof the processes or algorithms described herein may be performed in adifferent sequence, may be added, may be merged, and/or may be left outaltogether (for example, not all described operations or events arenecessary for the practice of the process or algorithm). Moreover, incertain embodiments, operations or events may be performed concurrently,for example, through multi-threaded processing, interrupt processing, ormultiple processors or processor cores or on other parallelarchitectures, rather than sequentially.

The various illustrative logical blocks, modules, routines, andalgorithm steps described in connection with the embodiments disclosedherein may be implemented as electronic hardware, computer software, orcombinations of both. To clearly illustrate this interchangeability ofhardware and software, various illustrative components, blocks, modules,and steps have been described above generally in terms of theirfunctionality. Whether such functionality is implemented as hardware orsoftware depends upon the particular application and design constraintsimposed on the overall system. The described functionality may beimplemented in varying ways for each particular application, but suchimplementation decisions should not be interpreted as causing adeparture from the scope of the disclosure.

The steps of a method, process, routine, or algorithm described inconnection with the embodiments disclosed herein may be embodieddirectly in hardware, in a software module executed by a processor, orin a combination of the two. A software module may reside in RAM memory,flash memory, ROM memory, EPROM memory, EEPROM memory, registers, harddisk, a removable disk, a CD-ROM, or any other form of a non-transitorycomputer-readable storage medium. An example storage medium may becoupled to the processor such that the processor may read informationfrom, and write information to, the storage medium. In the alternative,the storage medium may be integral to the processor. The processor andthe storage medium may reside in an ASIC. The ASIC may reside in a userterminal. In the alternative, the processor and the storage medium mayreside as discrete components in a user terminal.

Conditional language used herein, such as, among others, “can,” “could,”“might,” “may,” “for example,” and the like, unless specifically statedotherwise, or otherwise understood within the context as used, isgenerally intended to convey that certain embodiments include, whileother embodiments do not include, certain features, elements and/orsteps. Thus, such conditional language is not generally intended toimply that features, elements and/or steps are in any way required forone or more embodiments or that one or more embodiments necessarilyinclude logic for deciding, with or without author input or prompting,whether these features, elements and/or steps are included or are to beperformed in any particular embodiment. The terms “comprising,”“including,” “having,” and the like are synonymous and are usedinclusively, in an open-ended fashion, and do not exclude additionalelements, features, acts, operations, and so forth. Also, the term “or”is used in its inclusive sense (and not in its exclusive sense) so thatwhen used, for example, to connect a list of elements, the term “or”means one, some, or all of the elements in the list.

Conjunctive language such as the phrase “at least one of X, Y and Z,”unless specifically stated otherwise, is to be understood with thecontext as used in general to convey that an item, term, etc. may beeither X, Y, or Z, or a combination thereof. Thus, such conjunctivelanguage is not generally intended to imply that certain embodimentsrequire at least one of X, at least one of Y, and at least one of Z toeach be present.

While the above detailed description has shown, described, and pointedout novel features as applied to various embodiments, it may beunderstood that various omissions, substitutions, and changes in theform and details of the devices or processes illustrated may be madewithout departing from the spirit of the disclosure. As may berecognized, certain embodiments of the inventions described herein maybe embodied within a form that does not provide all of the features andbenefits set forth herein, as some features may be used or practicedseparately from others. The scope of certain inventions disclosed hereinis indicated by the appended claims rather than by the foregoingdescription. All changes which come within the meaning and range ofequivalency of the claims are to be embraced within their scope.

What is claimed is:
 1. A computer-implemented method comprising: asimplemented by one or more computer systems comprising computer hardwareand memory, the one or more computer systems configured with specificexecutable instructions, receiving, by the one or more computer systems,one or more voice commands from a requestor; triggering, based on theone or more voice commands, a request for an electronic consumer creditanalysis; accessing, from one or more electronic data stores, consumercredit data associated with a plurality of consumers with each of theconsumers being associated with a same demographic and/or geographicinformation as the requestor, wherein the requestor is one of theplurality of consumers; designating a plurality of high scorers from theplurality of consumers, the plurality of high scorers each havingassociated credit scores that exceed a predetermined threshold;obtaining, for each of a plurality of credit categories, an aggregatedhigh scorers score indicative of consumer credit data associated withthe plurality of high scorers; accessing a data structure associatedwith maintaining explanatory text; and providing, by the computingsystems for auditory output via a speaker associated with a user deviceof the requestor, the electronic consumer credit analysis including forat least one of the plurality of credit categories: explanatory textassociated with the credit category, the aggregated high scorers scorefor the credit category, and consumer credit data associated with therequestor for the credit category, wherein the one or more computersystems are configured to respond to user input associated with theelectronic consumer credit analysis, the user input comprising voicecommands received from the user device of the requestor.
 2. Thecomputer-implemented method of claim 1, wherein demographic informationcomprises one or more of: an age, a gender, an employment status, or anet worth, and wherein geographic information comprises one or more of:a neighborhood, a city, a county, a state, or a country.
 3. Thecomputer-implemented method of claim 1, wherein the request identifiesdemographic and/or geographic information indicated by the requestor. 4.The computer-implemented method of claim 3, wherein the requestidentifies a zip code provided by the requestor, and wherein thegeographic information is a location associated with the zip code. 5.The computer-implemented method of claim 1, further comprising:automatically determining, from consumer credit data associated with therequestor, demographic and/or geographic information associated with therequestor.
 6. The computer-implemented method of claim 5, wherein theconsumer credit data associated with the requestor identifies an addressof the requestor, and wherein determining geographic informationcomprises determining that the geographic information identifies a samestate as the address of the requestor.
 7. A non-transitory computerstorage media storing instructions that when executed by a system of oneor more computers cause the system to perform operations comprising:receiving, by the system, one or more voice commands from a requestor;triggering, based on the one or more voice commands, a request for anelectronic consumer credit analysis; accessing, from one or moreelectronic data stores, consumer credit data associated with a pluralityof consumers with each of the consumers being associated with a samedemographic and/or geographic information as the requestor, wherein therequestor is one of the plurality of consumers; designating a pluralityof high scorers from the plurality of consumers, the plurality of highscorers each having associated credit scores that exceed a predeterminedthreshold; obtaining, for each of a plurality of credit categories, anaggregated high scorers score indicative of consumer credit dataassociated with the plurality of high scorers; accessing a datastructure associated with maintaining explanatory text; and providing,by the system for auditory output via a speaker associated with a userdevice of the requestor, the electronic consumer credit analysisincluding for at least one of the plurality of credit categories:explanatory text associated with describing the credit category, theaggregated high scorers score for the credit category, and consumercredit data associated with the requestor for the credit category,wherein the one or more computer systems are configured to respond touser input associated with the electronic consumer credit analysis, theuser input comprising voice commands received from the user device ofthe requestor.
 8. The non-transitory computer storage media of claim 7,wherein demographic information comprises one or more of: an age, agender, an employment status, or a net worth, and wherein geographicinformation comprises one or more of: a neighborhood, a city, a county,a state, or a country.
 9. The non-transitory computer storage media ofclaim 7, wherein the request identifies demographic and/or geographicinformation indicated by the requester.
 10. The non-transitory computerstorage media of claim 9, wherein the request identifies a zip codeprovided by the requestor, and wherein the geographic information is alocation associated with the zip code.
 11. The non-transitory computerstorage media of claim 7, wherein the operations further comprise:automatically determining, from consumer credit data associated with therequestor, demographic and/or geographic information associated with therequestor.
 12. The non-transitory computer storage media of claim 11,wherein the consumer credit data associated with the requestoridentifies an address of the requestor, and wherein determininggeographic information comprises determining that the geographicinformation identifies a same state as the address of the requestor. 13.A system comprising one or more processors and non-transitory computerstorage media storing instructions that when executed by the one or moreprocessors, cause the one or more processors to perform operationscomprising: receiving one or more voice commands from a requestor;triggering, based on the one or more voice commands, a request for anelectronic consumer credit analysis; accessing, from one or moreelectronic data stores, consumer credit data associated with a pluralityof consumers with each of the consumers being associated with a samedemographic and/or geographic information as the requestor, wherein therequestor is one of the plurality of consumers; designating a pluralityof high scorers from the plurality of consumers, the plurality of highscorers each having associated credit scores that exceed a predeterminedthreshold; obtaining, for each of a plurality of credit categories, anaggregated high scorers score indicative of consumer credit dataassociated with the plurality of high scorers; accessing a datastructure associated with maintaining explanatory text; and providing,for auditory output via a speaker associated with a user device of therequestor, the electronic consumer credit analysis including for atleast one of the plurality of credit categories: explanatory textassociated with the credit category, the aggregated high scorers scorefor the credit category, and consumer credit data associated with therequestor for the credit category, wherein the one or more processorsare configured to respond to user input associated with the electronicconsumer credit analysis, the user input comprising voice commandsreceived from the user device of the requestor.